Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis

Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis

Blog Article

Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI).Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace identified by certain criteria have gained widespread use.However, methods employing second-order statistics as criteria often struggle to retain the signal of the small targets in the denoising results.Other 511 lunch bag methods utilizing high-order statistics encounter difficulties in effectively suppressing noise.

To tackle these challenges, we delve into a novel criterion to determine the projection subspace, and propose an innovative low-rank-based method that successfully preserves the spectral characteristic of small targets while significantly smok rpm c coils reducing noise.The experimental results on the synthetic and real datasets demonstrate the effectiveness of the proposed method, in terms of both small-target preservation and noise reduction.